From empirical to analytical: Soft-tissue tension gauging in total knee arthroplasty
Xiangdong Wu, Yunfeng Zhang, Zhuyi Ma, Qi Wang, Hongyi Shao, Dejin Yang, Yixin Zhou
- 发表年份
- 2025
- 引用次数
- 2
- 访问权限
- 开放获取
摘要
Soft-tissue balancing is essential for achieving optimal outcomes in total knee arthroplasty (TKA), significantly impacting postoperative joint function, patient satisfaction, and implant longevity. Despite advancements in surgical techniques, traditional methods for evaluating soft-tissue tension remain largely subjective, leading to inconsistent outcomes and patient dissatisfaction. Recent technological developments, particularly the integration of digital devices, have shown promise in transforming soft-tissue balancing from a subjective art into a reproducible science. This manuscript is a narrative review that systematically summarizes the historical and technological evolution of soft-tissue tension gauging methods in TKA, encompassing experiential methods, mechanical tensors, and contemporary digital sensors. We critically discuss the strengths, limitations, and available clinical evidence for each method. Furthermore, this review highlights the integration of robotic systems and provides insights into future translational strategies, emphasizing artificial-intelligence-driven personalized soft-tissue balancing as a promising therapeutic direction. This review further comprehensively discusses soft-tissue tension gauging methods in TKA, providing a clear understanding of their evolution from subjective assessments to objective digital technologies. This study provides a robust theoretical foundation for the clinical application of digital tensors and robotic technologies. Integrating these technologies with artificial intelligence can effectively transform soft-tissue balancing strategies, thereby enhancing surgical precision, patient satisfaction, and clinical outcomes in TKA.
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